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Special Issue "Internet of Things in Healthcare Applications"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 15 July 2019

Special Issue Editors

Guest Editor
Prof. Craig Michie

Department of Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, Scotland, UK
Website | E-Mail
Interests: wireless sensor networks; Internet of Things; machine learning; artificial intelligence
Guest Editor
Dr. Christos Tachtatzis

Department of Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, Scotland, UK
Website | E-Mail
Interests: Internet of Things; cloud and edge computing; machine learning; artificial intelligence; ultra-low power networks
Guest Editor
Prof. Roma Maguire

Department of Computer and Information Systems, University of Strathclyde, 26 Richmond Street, Glasgow G1 1XH, Scotland, UK
Website | E-Mail
Interests: Digital Health and Care, Supportive Care, Internet of Things, Clinical Decision Making, Remote Patient Monitoring, Real World Evidence

Special Issue Information

Dear Colleagues,

The proliferation of wireless sensor networks has enabled a wide range of healthcare applications to continuously monitor patients and enable the health and social services to become more preventative and anticipatory—keeping people out of hospital care and as well as possible in their own homes. To effectively realise these services, sensors and devices must operate in a non-intrusive fashion for the users and record data in a contextualised, reliable and timely fashion. Furthermore, the large amounts of data collected cannot be screened manually and intelligent decision support technologies are necessary to automatically identify users that require attention and enable professionals to quickly intervene. It is therefore necessary to enable this intelligence at all levels of the technology stack, from the sensor device itself all the way to the data aggregation and visualisation platform. This Special Issue seeks novel articles on:

  • Sensor for physical, physiological, psychological, cognitive, and behavioural processes
  • Systems integration
  • Artificial intelligence and machine learning
  • Internet of Wearable Things
  • Telecare

Prof. Craig Michie
Dr. Christos Tachtatzis
Prof. Roma Maguire
Guest Editors


Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensor for physical, physiological, psychological, cognitive, and behavioural processes
  • Systems integration
  • Artificial intelligence and machine learning
  • Internet of Wearable Things
  • Telecare

Published Papers (1 paper)

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Research

Open AccessArticle Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks
Sensors 2019, 19(7), 1644; https://doi.org/10.3390/s19071644
Received: 9 March 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 6 April 2019
PDF Full-text (928 KB) | HTML Full-text | XML Full-text
Abstract
Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus [...] Read more.
Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain. Full article
(This article belongs to the Special Issue Internet of Things in Healthcare Applications)
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